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Case studies for practical food effect assessments across BCS/BDDCS class compounds using in silico, in vitro, and preclinical in vivo data

Heimbach, Tycho and Xia, Binfeng and Lin, Tsu-han and He, Handan (2013) Case studies for practical food effect assessments across BCS/BDDCS class compounds using in silico, in vitro, and preclinical in vivo data. AAPS Journal, 15 (1). pp. 143-158. ISSN 1550-7416

Abstract

Practical food effect predictions and assessments were described using in silico, in vitro, and/or in vivo preclinical data to anticipate food effects and Biopharmaceutics Classification System (BCS)/Biopharmaceutics Drug Disposition Classification System (BDDCS) class across drug development stages depending on available data: (1) limited in silico and in vitro data in early discovery; (2) preclinical in vivo pharmacokinetic, absorption, and metabolism data at candidate selection; and (3) physiologically based absorption modeling using biorelevant solubility and precipitation data to quantitatively predict human food effects, oral absorption, and pharmacokinetic profiles for early clinical studies. Early food effect predictions used calculated or measured physicochemical properties to establish a preliminary BCS/BDDCS class. A rat-based preclinical BCS/BDDCS classification used rat in vivo fraction absorbed and metabolism data. Biorelevant solubility and precipitation kinetic data were generated via animal pharmacokinetic studies using advanced compartmental absorption and transit (ACAT) models or in vitro methods. Predicted human plasma concentration-time profiles and the magnitude of the food effects were compared with observed clinical data for assessment of simulation accuracy. Simulations and analyses successfully identified potential food effects across BCS/BDDCS classes 1-4 compounds with an average fold error less than 1.6 in most cases. ACAT physiological absorption models accurately predicted positive food effects in human for poorly soluble bases after oral dosage forms. Integration of solubility, precipitation time, and metabolism data allowed confident identification of a compound's BCS/BDDCS class, its likely food effects, along with prediction of human exposure profiles under fast and fed conditions. © 2012 American Association of Pharmaceutical Scientists.

Item Type: Article
Additional Information: There are no structures, Novartis compounds names or targets.
Keywords: absorption modeling BCS/BDDCS food effect prediction human PBPK model oral bioavailability
Date Deposited: 22 Nov 2017 00:45
Last Modified: 25 Jan 2019 00:46
URI: https://oak.novartis.com/id/eprint/7989

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